Alternatives Methodologies for Lean Manufacturing: Genetic Algorithm

Abstract

Global competition has forced industries to be in a constant search of technology; however, any technology that involves an advantage is temporary. Nowadays, a computer is seen in every enterprise mostly with the objective of being an advantage and as a tool to facilitate tasks (besides entertaining). Thus, a question arises, is it the most modern and expensive computer a significant advantage by default? I guess not today. The development of computers has arguably been the most radical achievement in the history of science and technology. The first computer scientists were motivated in large part by visions of creating computers programs with intelligence, mainly focusing to modeling the brain, imitating human behavior, and simulating biological evolution. This matter had its takeoff in the early 1980s, mostly by the advances in computational power; with evolutionary computation as one of the first ones, where “genetic algorithm” is the most prominent example (Mitchell 1998). Therefore, the “best” technologies are not limited to the acquisition of modern equipment or expensive machinery, but also include the progress and implementation of intelligent computational approaches such as the Genetic Algorithm. Therefore, the aim of this chapter is to show some applications of Genetic algorithm in the manufacturing sector in order to obtain a lean environment. This chapter begins with a brief definition of what a Genetic Algorithm is, its basic elements, and some numerical examples in order to facilitate its application. Next, two cases taken from the manufacturing industry of Juarez, Mexico are illustrated.